Overview

Dataset statistics

Number of variables27
Number of observations539
Missing cells89
Missing cells (%)0.6%
Duplicate rows18
Duplicate rows (%)3.3%
Total size in memory113.8 KiB
Average record size in memory216.2 B

Variable types

Categorical10
Numeric12
Text4
DateTime1

Alerts

artist_id has constant value ""Constant
artist_name has constant value ""Constant
artist_popularity has constant value ""Constant
Dataset has 18 (3.3%) duplicate rowsDuplicates
disc_number is highly imbalanced (79.8%)Imbalance
explicit is highly imbalanced (72.5%)Imbalance
audio_features.mode is highly imbalanced (57.3%)Imbalance
audio_features.time_signature is highly imbalanced (82.7%)Imbalance
track_id has 8 (1.5%) missing valuesMissing
track_name has 7 (1.3%) missing valuesMissing
album_name has 62 (11.5%) missing valuesMissing
audio_features.key has 97 (18.0%) zerosZeros

Reproduction

Analysis started2024-01-10 04:22:09.824444
Analysis finished2024-01-10 04:22:39.119627
Duration29.3 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

disc_number
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
1
522 
2
 
17

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters539
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 522
96.8%
2 17
 
3.2%

Length

2024-01-09T23:22:39.257183image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-09T23:22:39.424289image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1 522
96.8%
2 17
 
3.2%

Most occurring characters

ValueCountFrequency (%)
1 522
96.8%
2 17
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 539
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 522
96.8%
2 17
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Common 539
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 522
96.8%
2 17
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 539
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 522
96.8%
2 17
 
3.2%

duration_ms
Real number (ℝ)

Distinct364
Distinct (%)67.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean236003.73
Minimum-223093
Maximum613026
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)0.4%
Memory size4.3 KiB
2024-01-09T23:22:39.623533image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-223093
5-th percentile174782
Q1209486.5
median233626
Q3259045.5
95-th percentile315148.7
Maximum613026
Range836119
Interquartile range (IQR)49559

Descriptive statistics

Standard deviation55019.871
Coefficient of variation (CV)0.23313137
Kurtosis16.911475
Mean236003.73
Median Absolute Deviation (MAD)24649
Skewness-0.87552564
Sum1.2720601 × 108
Variance3.0271862 × 109
MonotonicityNot monotonic
2024-01-09T23:22:39.866789image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
231000 6
 
1.1%
212600 5
 
0.9%
247533 5
 
0.9%
235800 5
 
0.9%
271000 4
 
0.7%
202960 3
 
0.6%
255893 3
 
0.6%
200690 3
 
0.6%
256124 3
 
0.6%
194206 3
 
0.6%
Other values (354) 499
92.6%
ValueCountFrequency (%)
-223093 1
0.2%
-107133 1
0.2%
10 1
0.2%
1000 1
0.2%
3000 1
0.2%
83253 1
0.2%
131186 1
0.2%
146436 2
0.4%
148781 2
0.4%
150440 2
0.4%
ValueCountFrequency (%)
613026 1
0.2%
405906 1
0.2%
404680 1
0.2%
403933 1
0.2%
403887 1
0.2%
389213 1
0.2%
376466 1
0.2%
369120 1
0.2%
369066 1
0.2%
367146 2
0.4%

explicit
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
False
480 
True
54 
No
 
4
Si
 
1

Length

Max length5
Median length5
Mean length4.8719852
Min length2

Characters and Unicode

Total characters2626
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowFalse
2nd rowFalse
3rd rowFalse
4th rowFalse
5th rowFalse

Common Values

ValueCountFrequency (%)
False 480
89.1%
True 54
 
10.0%
No 4
 
0.7%
Si 1
 
0.2%

Length

2024-01-09T23:22:40.091802image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-09T23:22:40.273785image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
false 480
89.1%
true 54
 
10.0%
no 4
 
0.7%
si 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e 534
20.3%
F 480
18.3%
a 480
18.3%
l 480
18.3%
s 480
18.3%
T 54
 
2.1%
r 54
 
2.1%
u 54
 
2.1%
N 4
 
0.2%
o 4
 
0.2%
Other values (2) 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2087
79.5%
Uppercase Letter 539
 
20.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 534
25.6%
a 480
23.0%
l 480
23.0%
s 480
23.0%
r 54
 
2.6%
u 54
 
2.6%
o 4
 
0.2%
i 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
F 480
89.1%
T 54
 
10.0%
N 4
 
0.7%
S 1
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 2626
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 534
20.3%
F 480
18.3%
a 480
18.3%
l 480
18.3%
s 480
18.3%
T 54
 
2.1%
r 54
 
2.1%
u 54
 
2.1%
N 4
 
0.2%
o 4
 
0.2%
Other values (2) 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2626
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 534
20.3%
F 480
18.3%
a 480
18.3%
l 480
18.3%
s 480
18.3%
T 54
 
2.1%
r 54
 
2.1%
u 54
 
2.1%
N 4
 
0.2%
o 4
 
0.2%
Other values (2) 2
 
0.1%

track_number
Real number (ℝ)

Distinct46
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.280148
Minimum1
Maximum46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-01-09T23:22:40.485782image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median10
Q315
95-th percentile25.1
Maximum46
Range45
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.9656206
Coefficient of variation (CV)0.70616274
Kurtosis2.8653451
Mean11.280148
Median Absolute Deviation (MAD)5
Skewness1.367575
Sum6080
Variance63.451111
MonotonicityNot monotonic
2024-01-09T23:22:40.883826image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
8 29
 
5.4%
1 28
 
5.2%
6 28
 
5.2%
7 28
 
5.2%
2 28
 
5.2%
5 28
 
5.2%
3 28
 
5.2%
4 28
 
5.2%
11 27
 
5.0%
12 27
 
5.0%
Other values (36) 260
48.2%
ValueCountFrequency (%)
1 28
5.2%
2 28
5.2%
3 28
5.2%
4 28
5.2%
5 28
5.2%
6 28
5.2%
7 28
5.2%
8 29
5.4%
9 27
5.0%
10 27
5.0%
ValueCountFrequency (%)
46 1
0.2%
45 1
0.2%
44 1
0.2%
43 1
0.2%
42 1
0.2%
41 1
0.2%
40 1
0.2%
39 1
0.2%
38 1
0.2%
37 1
0.2%

track_popularity
Real number (ℝ)

Distinct73
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.918367
Minimum-92
Maximum152
Zeros3
Zeros (%)0.6%
Negative6
Negative (%)1.1%
Memory size4.3 KiB
2024-01-09T23:22:41.118408image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-92
5-th percentile34
Q151
median69
Q377
95-th percentile85.1
Maximum152
Range244
Interquartile range (IQR)26

Descriptive statistics

Standard deviation22.498757
Coefficient of variation (CV)0.35758647
Kurtosis15.554817
Mean62.918367
Median Absolute Deviation (MAD)11
Skewness-2.7689799
Sum33913
Variance506.19407
MonotonicityNot monotonic
2024-01-09T23:22:41.446334image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 24
 
4.5%
72 20
 
3.7%
82 18
 
3.3%
80 18
 
3.3%
76 17
 
3.2%
78 17
 
3.2%
68 16
 
3.0%
71 16
 
3.0%
74 15
 
2.8%
77 15
 
2.8%
Other values (63) 363
67.3%
ValueCountFrequency (%)
-92 1
 
0.2%
-85 1
 
0.2%
-75 1
 
0.2%
-71 1
 
0.2%
-70 1
 
0.2%
-69 1
 
0.2%
0 3
0.6%
30 1
 
0.2%
31 1
 
0.2%
32 3
0.6%
ValueCountFrequency (%)
152 1
 
0.2%
99 2
 
0.4%
94 1
 
0.2%
92 2
 
0.4%
91 3
0.6%
90 2
 
0.4%
89 1
 
0.2%
88 4
0.7%
87 6
1.1%
86 5
0.9%

track_id
Text

MISSING 

Distinct512
Distinct (%)96.4%
Missing8
Missing (%)1.5%
Memory size4.3 KiB
2024-01-09T23:22:41.724297image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters11682
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique493 ?
Unique (%)92.8%

Sample

1st row4WUepByoeqcedHoYhSNHRt
2nd row0108kcWLnn2HlH2kedi1gn
3rd row3Vpk1hfMAQme8VJ0SNRSkd
4th row1OcSfkeCg9hRC2sFKB4IMJ
5th row2k0ZEeAqzvYMcx9Qt5aClQ
ValueCountFrequency (%)
43ra71bccxfgd4c8gopiln 2
 
0.4%
5hqsxkfgbxjzo9ucwd11so 2
 
0.4%
1dgr1c8crmldpv6mpbimsi 2
 
0.4%
3xyjscvfxbyb61dyhtwiby 2
 
0.4%
3phkh7d0lzm2aldutz2x37 2
 
0.4%
1fzauuvbzlhz1ljax9pty6 2
 
0.4%
1smiq65isabpto6gpflbym 2
 
0.4%
2rk4jlnc2tpmze2af99d45 2
 
0.4%
1symezit3h8uzfibcs3tyi 2
 
0.4%
6rrnnciqgzexnqk8sq9yv5 2
 
0.4%
Other values (502) 511
96.2%
2024-01-09T23:22:42.194303image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 268
 
2.3%
2 255
 
2.2%
0 244
 
2.1%
1 241
 
2.1%
5 238
 
2.0%
7 236
 
2.0%
3 225
 
1.9%
6 220
 
1.9%
9 208
 
1.8%
c 204
 
1.7%
Other values (52) 9343
80.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4754
40.7%
Uppercase Letter 4628
39.6%
Decimal Number 2300
19.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 204
 
4.3%
y 203
 
4.3%
q 200
 
4.2%
k 197
 
4.1%
z 195
 
4.1%
m 193
 
4.1%
x 191
 
4.0%
f 189
 
4.0%
g 189
 
4.0%
d 187
 
3.9%
Other values (16) 2806
59.0%
Uppercase Letter
ValueCountFrequency (%)
V 194
 
4.2%
U 192
 
4.1%
Y 191
 
4.1%
I 190
 
4.1%
R 189
 
4.1%
Q 188
 
4.1%
P 187
 
4.0%
K 186
 
4.0%
M 186
 
4.0%
A 185
 
4.0%
Other values (16) 2740
59.2%
Decimal Number
ValueCountFrequency (%)
4 268
11.7%
2 255
11.1%
0 244
10.6%
1 241
10.5%
5 238
10.3%
7 236
10.3%
3 225
9.8%
6 220
9.6%
9 208
9.0%
8 165
7.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 9382
80.3%
Common 2300
 
19.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 204
 
2.2%
y 203
 
2.2%
q 200
 
2.1%
k 197
 
2.1%
z 195
 
2.1%
V 194
 
2.1%
m 193
 
2.1%
U 192
 
2.0%
Y 191
 
2.0%
x 191
 
2.0%
Other values (42) 7422
79.1%
Common
ValueCountFrequency (%)
4 268
11.7%
2 255
11.1%
0 244
10.6%
1 241
10.5%
5 238
10.3%
7 236
10.3%
3 225
9.8%
6 220
9.6%
9 208
9.0%
8 165
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11682
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 268
 
2.3%
2 255
 
2.2%
0 244
 
2.1%
1 241
 
2.1%
5 238
 
2.0%
7 236
 
2.0%
3 225
 
1.9%
6 220
 
1.9%
9 208
 
1.8%
c 204
 
1.7%
Other values (52) 9343
80.0%

track_name
Text

MISSING 

Distinct331
Distinct (%)62.2%
Missing7
Missing (%)1.3%
Memory size4.3 KiB
2024-01-09T23:22:42.658302image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length84
Median length56
Mean length22.030075
Min length2

Characters and Unicode

Total characters11720
Distinct characters67
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique180 ?
Unique (%)33.8%

Sample

1st rowWelcome To New York (Taylor's Version)
2nd rowBlank Space (Taylor's Version)
3rd rowStyle (Taylor's Version)
4th rowOut Of The Woods (Taylor's Version)
5th rowAll You Had To Do Was Stay (Taylor's Version)
ValueCountFrequency (%)
version 127
 
6.3%
the 119
 
5.9%
taylor's 79
 
3.9%
69
 
3.4%
taylor’s 42
 
2.1%
you 41
 
2.0%
from 39
 
1.9%
vault 31
 
1.5%
feat 30
 
1.5%
i 29
 
1.4%
Other values (413) 1417
70.0%
2024-01-09T23:22:43.368322image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1491
 
12.7%
e 1062
 
9.1%
o 814
 
6.9%
r 690
 
5.9%
a 655
 
5.6%
s 585
 
5.0%
n 560
 
4.8%
i 535
 
4.6%
t 482
 
4.1%
l 449
 
3.8%
Other values (57) 4397
37.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7779
66.4%
Uppercase Letter 1639
 
14.0%
Space Separator 1491
 
12.7%
Other Punctuation 215
 
1.8%
Close Punctuation 187
 
1.6%
Open Punctuation 187
 
1.6%
Decimal Number 104
 
0.9%
Dash Punctuation 63
 
0.5%
Final Punctuation 53
 
0.5%
Initial Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1062
13.7%
o 814
10.5%
r 690
8.9%
a 655
 
8.4%
s 585
 
7.5%
n 560
 
7.2%
i 535
 
6.9%
t 482
 
6.2%
l 449
 
5.8%
h 307
 
3.9%
Other values (15) 1640
21.1%
Uppercase Letter
ValueCountFrequency (%)
T 294
17.9%
V 164
 
10.0%
S 126
 
7.7%
B 89
 
5.4%
W 87
 
5.3%
F 78
 
4.8%
L 77
 
4.7%
I 74
 
4.5%
M 70
 
4.3%
O 69
 
4.2%
Other values (14) 511
31.2%
Other Punctuation
ValueCountFrequency (%)
' 104
48.4%
. 60
27.9%
/ 18
 
8.4%
, 9
 
4.2%
& 8
 
3.7%
! 6
 
2.8%
? 6
 
2.8%
" 4
 
1.9%
Decimal Number
ValueCountFrequency (%)
1 35
33.7%
0 32
30.8%
2 29
27.9%
8 8
 
7.7%
Space Separator
ValueCountFrequency (%)
1491
100.0%
Close Punctuation
ValueCountFrequency (%)
) 187
100.0%
Open Punctuation
ValueCountFrequency (%)
( 187
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 63
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 53
100.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9418
80.4%
Common 2302
 
19.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1062
 
11.3%
o 814
 
8.6%
r 690
 
7.3%
a 655
 
7.0%
s 585
 
6.2%
n 560
 
5.9%
i 535
 
5.7%
t 482
 
5.1%
l 449
 
4.8%
h 307
 
3.3%
Other values (39) 3279
34.8%
Common
ValueCountFrequency (%)
1491
64.8%
) 187
 
8.1%
( 187
 
8.1%
' 104
 
4.5%
- 63
 
2.7%
. 60
 
2.6%
’ 53
 
2.3%
1 35
 
1.5%
0 32
 
1.4%
2 29
 
1.3%
Other values (8) 61
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11665
99.5%
Punctuation 55
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1491
 
12.8%
e 1062
 
9.1%
o 814
 
7.0%
r 690
 
5.9%
a 655
 
5.6%
s 585
 
5.0%
n 560
 
4.8%
i 535
 
4.6%
t 482
 
4.1%
l 449
 
3.8%
Other values (55) 4342
37.2%
Punctuation
ValueCountFrequency (%)
’ 53
96.4%
‘ 2
 
3.6%
Distinct267
Distinct (%)49.7%
Missing2
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean0.58724209
Minimum0.243
Maximum0.897
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-01-09T23:22:43.610548image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.243
5-th percentile0.374
Q10.517
median0.595
Q30.661
95-th percentile0.777
Maximum0.897
Range0.654
Interquartile range (IQR)0.144

Descriptive statistics

Standard deviation0.11685757
Coefficient of variation (CV)0.19899386
Kurtosis0.016301277
Mean0.58724209
Median Absolute Deviation (MAD)0.069
Skewness-0.21514178
Sum315.349
Variance0.013655691
MonotonicityNot monotonic
2024-01-09T23:22:43.856549image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.602 9
 
1.7%
0.61 7
 
1.3%
0.546 6
 
1.1%
0.535 6
 
1.1%
0.575 6
 
1.1%
0.649 6
 
1.1%
0.553 6
 
1.1%
0.359 5
 
0.9%
0.624 5
 
0.9%
0.559 5
 
0.9%
Other values (257) 476
88.3%
ValueCountFrequency (%)
0.243 1
0.2%
0.292 2
0.4%
0.298 1
0.2%
0.31 2
0.4%
0.313 2
0.4%
0.316 2
0.4%
0.317 2
0.4%
0.327 1
0.2%
0.334 1
0.2%
0.335 1
0.2%
ValueCountFrequency (%)
0.897 2
0.4%
0.87 1
 
0.2%
0.867 1
 
0.2%
0.843 3
0.6%
0.828 1
 
0.2%
0.824 2
0.4%
0.815 1
 
0.2%
0.811 2
0.4%
0.81 1
 
0.2%
0.8 3
0.6%

audio_features.energy
Real number (ℝ)

Distinct348
Distinct (%)64.8%
Missing2
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean0.57306518
Minimum0.118
Maximum0.949
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-01-09T23:22:44.096983image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.118
5-th percentile0.248
Q10.436
median0.589
Q30.729
95-th percentile0.855
Maximum0.949
Range0.831
Interquartile range (IQR)0.293

Descriptive statistics

Standard deviation0.1923086
Coefficient of variation (CV)0.33557894
Kurtosis-0.77669357
Mean0.57306518
Median Absolute Deviation (MAD)0.144
Skewness-0.23968857
Sum307.736
Variance0.036982598
MonotonicityNot monotonic
2024-01-09T23:22:44.330940image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.634 6
 
1.1%
0.488 6
 
1.1%
0.386 5
 
0.9%
0.777 5
 
0.9%
0.747 5
 
0.9%
0.816 4
 
0.7%
0.459 4
 
0.7%
0.855 4
 
0.7%
0.361 4
 
0.7%
0.61 4
 
0.7%
Other values (338) 490
90.9%
ValueCountFrequency (%)
0.118 1
0.2%
0.128 1
0.2%
0.131 1
0.2%
0.151 1
0.2%
0.155 1
0.2%
0.156 1
0.2%
0.16 1
0.2%
0.161 1
0.2%
0.166 1
0.2%
0.172 1
0.2%
ValueCountFrequency (%)
0.949 1
0.2%
0.944 2
0.4%
0.934 1
0.2%
0.933 1
0.2%
0.917 2
0.4%
0.915 2
0.4%
0.909 1
0.2%
0.908 1
0.2%
0.902 1
0.2%
0.899 1
0.2%

audio_features.key
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)2.2%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean4.5873606
Minimum0
Maximum11
Zeros97
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-01-09T23:22:44.520944image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q37
95-th percentile10
Maximum11
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.2460817
Coefficient of variation (CV)0.70761425
Kurtosis-1.0943395
Mean4.5873606
Median Absolute Deviation (MAD)3
Skewness0.061883653
Sum2468
Variance10.537047
MonotonicityNot monotonic
2024-01-09T23:22:44.697626image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 97
18.0%
7 95
17.6%
5 66
12.2%
4 62
11.5%
2 58
10.8%
9 38
 
7.1%
1 26
 
4.8%
6 24
 
4.5%
8 23
 
4.3%
10 22
 
4.1%
Other values (2) 27
 
5.0%
ValueCountFrequency (%)
0 97
18.0%
1 26
 
4.8%
2 58
10.8%
3 13
 
2.4%
4 62
11.5%
5 66
12.2%
6 24
 
4.5%
7 95
17.6%
8 23
 
4.3%
9 38
 
7.1%
ValueCountFrequency (%)
11 14
 
2.6%
10 22
 
4.1%
9 38
 
7.1%
8 23
 
4.3%
7 95
17.6%
6 24
 
4.5%
5 66
12.2%
4 62
11.5%
3 13
 
2.4%
2 58
10.8%

audio_features.loudness
Real number (ℝ)

Distinct448
Distinct (%)83.4%
Missing2
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean-7.5206387
Minimum-17.932
Maximum-1.909
Zeros0
Zeros (%)0.0%
Negative537
Negative (%)99.6%
Memory size4.3 KiB
2024-01-09T23:22:44.909676image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-17.932
5-th percentile-12.9256
Q1-9.287
median-6.942
Q3-5.376
95-th percentile-3.602
Maximum-1.909
Range16.023
Interquartile range (IQR)3.911

Descriptive statistics

Standard deviation2.9331578
Coefficient of variation (CV)-0.39001445
Kurtosis0.045956858
Mean-7.5206387
Median Absolute Deviation (MAD)1.951
Skewness-0.62719954
Sum-4038.583
Variance8.6034147
MonotonicityNot monotonic
2024-01-09T23:22:45.194018image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-10.813 4
 
0.7%
-6.112 3
 
0.6%
-4.991 3
 
0.6%
-5.797 3
 
0.6%
-6.942 3
 
0.6%
-3.185 2
 
0.4%
-3.863 2
 
0.4%
-2.976 2
 
0.4%
-3.75 2
 
0.4%
-7.091 2
 
0.4%
Other values (438) 511
94.8%
ValueCountFrequency (%)
-17.932 1
0.2%
-16.394 1
0.2%
-15.91 1
0.2%
-15.489 1
0.2%
-15.48 1
0.2%
-15.434 1
0.2%
-15.065 1
0.2%
-15.064 1
0.2%
-15.01 2
0.4%
-14.958 1
0.2%
ValueCountFrequency (%)
-1.909 1
0.2%
-1.953 1
0.2%
-2.098 1
0.2%
-2.347 1
0.2%
-2.608 1
0.2%
-2.622 1
0.2%
-2.641 2
0.4%
-2.846 1
0.2%
-2.871 1
0.2%
-2.881 1
0.2%

audio_features.mode
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
1
492 
0
 
47

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters539
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 492
91.3%
0 47
 
8.7%

Length

2024-01-09T23:22:45.439021image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-09T23:22:45.596947image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1 492
91.3%
0 47
 
8.7%

Most occurring characters

ValueCountFrequency (%)
1 492
91.3%
0 47
 
8.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 539
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 492
91.3%
0 47
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
Common 539
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 492
91.3%
0 47
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 539
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 492
91.3%
0 47
 
8.7%
Distinct292
Distinct (%)54.3%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.05770948
Minimum0.0231
Maximum0.912
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-01-09T23:22:45.788012image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.0231
5-th percentile0.0263
Q10.030525
median0.03775
Q30.0564
95-th percentile0.15945
Maximum0.912
Range0.8889
Interquartile range (IQR)0.025875

Descriptive statistics

Standard deviation0.073145834
Coefficient of variation (CV)1.2674839
Kurtosis58.491295
Mean0.05770948
Median Absolute Deviation (MAD)0.0099
Skewness6.7363652
Sum31.0477
Variance0.005350313
MonotonicityNot monotonic
2024-01-09T23:22:46.030049image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0275 7
 
1.3%
0.0308 6
 
1.1%
0.0264 6
 
1.1%
0.0281 5
 
0.9%
0.0273 5
 
0.9%
0.031 5
 
0.9%
0.0274 5
 
0.9%
0.0323 5
 
0.9%
0.0293 5
 
0.9%
0.0363 5
 
0.9%
Other values (282) 484
89.8%
ValueCountFrequency (%)
0.0231 1
 
0.2%
0.0234 1
 
0.2%
0.0239 1
 
0.2%
0.0243 3
0.6%
0.0244 1
 
0.2%
0.0245 1
 
0.2%
0.0246 1
 
0.2%
0.025 1
 
0.2%
0.0251 1
 
0.2%
0.0252 1
 
0.2%
ValueCountFrequency (%)
0.912 1
0.2%
0.721 1
0.2%
0.589 1
0.2%
0.519 2
0.4%
0.39 1
0.2%
0.364 1
0.2%
0.363 1
0.2%
0.245 2
0.4%
0.239 2
0.4%
0.201 1
0.2%
Distinct401
Distinct (%)74.5%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.33780623
Minimum-0.00354
Maximum5
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)0.4%
Memory size4.3 KiB
2024-01-09T23:22:46.266011image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-0.00354
5-th percentile0.002303
Q10.03625
median0.1675
Q30.66375
95-th percentile0.907
Maximum5
Range5.00354
Interquartile range (IQR)0.6275

Descriptive statistics

Standard deviation0.39529207
Coefficient of variation (CV)1.170174
Kurtosis34.978306
Mean0.33780623
Median Absolute Deviation (MAD)0.16079
Skewness3.5477485
Sum181.73975
Variance0.15625582
MonotonicityNot monotonic
2024-01-09T23:22:46.495052image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.101 5
 
0.9%
0.13 5
 
0.9%
0.481 3
 
0.6%
0.819 3
 
0.6%
0.964 3
 
0.6%
0.553 3
 
0.6%
0.298 3
 
0.6%
0.0129 3
 
0.6%
0.00471 3
 
0.6%
0.0167 3
 
0.6%
Other values (391) 504
93.5%
ValueCountFrequency (%)
-0.00354 1
0.2%
-0.000537 1
0.2%
0.000184 1
0.2%
0.000191 1
0.2%
0.000197 1
0.2%
0.000315 2
0.4%
0.000328 1
0.2%
0.000418 1
0.2%
0.000421 1
0.2%
0.000443 1
0.2%
ValueCountFrequency (%)
5 1
 
0.2%
2 1
 
0.2%
1.5 1
 
0.2%
0.971 2
0.4%
0.967 2
0.4%
0.966 1
 
0.2%
0.964 3
0.6%
0.962 1
 
0.2%
0.946 1
 
0.2%
0.942 1
 
0.2%
Distinct240
Distinct (%)44.5%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
2024-01-09T23:22:46.841015image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length4.7439703
Min length1

Characters and Unicode

Total characters2557
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique179 ?
Unique (%)33.2%

Sample

1st row3.66e-05
2nd row0
3rd row0.0197
4th row5.59e-05
5th row0
ValueCountFrequency (%)
0 235
43.6%
2.06e-05 4
 
0.7%
2.67e-05 3
 
0.6%
1.64e-06 3
 
0.6%
2.46e-05 3
 
0.6%
9.48e-06 2
 
0.4%
6.77e-06 2
 
0.4%
0.000326 2
 
0.4%
1.5e-05 2
 
0.4%
7.9e-06 2
 
0.4%
Other values (230) 281
52.1%
2024-01-09T23:22:47.419061image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 838
32.8%
. 303
 
11.8%
- 192
 
7.5%
e 191
 
7.5%
6 170
 
6.6%
5 170
 
6.6%
1 165
 
6.5%
2 112
 
4.4%
3 103
 
4.0%
4 91
 
3.6%
Other values (4) 222
 
8.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1870
73.1%
Other Punctuation 303
 
11.8%
Dash Punctuation 192
 
7.5%
Lowercase Letter 192
 
7.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 838
44.8%
6 170
 
9.1%
5 170
 
9.1%
1 165
 
8.8%
2 112
 
6.0%
3 103
 
5.5%
4 91
 
4.9%
7 90
 
4.8%
8 67
 
3.6%
9 64
 
3.4%
Lowercase Letter
ValueCountFrequency (%)
e 191
99.5%
x 1
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 303
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 192
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2365
92.5%
Latin 192
 
7.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 838
35.4%
. 303
 
12.8%
- 192
 
8.1%
6 170
 
7.2%
5 170
 
7.2%
1 165
 
7.0%
2 112
 
4.7%
3 103
 
4.4%
4 91
 
3.8%
7 90
 
3.8%
Other values (2) 131
 
5.5%
Latin
ValueCountFrequency (%)
e 191
99.5%
x 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2557
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 838
32.8%
. 303
 
11.8%
- 192
 
7.5%
e 191
 
7.5%
6 170
 
6.6%
5 170
 
6.6%
1 165
 
6.5%
2 112
 
4.4%
3 103
 
4.0%
4 91
 
3.6%
Other values (4) 222
 
8.7%

audio_features.liveness
Real number (ℝ)

Distinct271
Distinct (%)50.4%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.16330781
Minimum0.0335
Maximum0.931
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-01-09T23:22:47.663219image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.0335
5-th percentile0.0663
Q10.0965
median0.115
Q30.16225
95-th percentile0.3752
Maximum0.931
Range0.8975
Interquartile range (IQR)0.06575

Descriptive statistics

Standard deviation0.14180033
Coefficient of variation (CV)0.86830097
Kurtosis11.903826
Mean0.16330781
Median Absolute Deviation (MAD)0.025
Skewness3.2553196
Sum87.8596
Variance0.020107333
MonotonicityNot monotonic
2024-01-09T23:22:47.884637image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.108 13
 
2.4%
0.111 12
 
2.2%
0.101 11
 
2.0%
0.106 11
 
2.0%
0.115 10
 
1.9%
0.105 8
 
1.5%
0.123 8
 
1.5%
0.121 8
 
1.5%
0.114 8
 
1.5%
0.118 8
 
1.5%
Other values (261) 441
81.8%
ValueCountFrequency (%)
0.0335 1
0.2%
0.0357 1
0.2%
0.0391 1
0.2%
0.0419 2
0.4%
0.0437 1
0.2%
0.0473 1
0.2%
0.0477 1
0.2%
0.054 1
0.2%
0.0574 1
0.2%
0.0576 1
0.2%
ValueCountFrequency (%)
0.931 1
0.2%
0.918 1
0.2%
0.889 1
0.2%
0.884 1
0.2%
0.867 1
0.2%
0.865 1
0.2%
0.837 1
0.2%
0.83 1
0.2%
0.815 1
0.2%
0.795 1
0.2%

audio_features.valence
Real number (ℝ)

Distinct326
Distinct (%)60.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.39840983
Minimum0.0374
Maximum0.943
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-01-09T23:22:48.102571image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.0374
5-th percentile0.107
Q10.23
median0.386
Q30.535
95-th percentile0.7464
Maximum0.943
Range0.9056
Interquartile range (IQR)0.305

Descriptive statistics

Standard deviation0.1994087
Coefficient of variation (CV)0.5005115
Kurtosis-0.44167846
Mean0.39840983
Median Absolute Deviation (MAD)0.153
Skewness0.37971005
Sum214.7429
Variance0.039763831
MonotonicityNot monotonic
2024-01-09T23:22:48.316379image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.545 7
 
1.3%
0.374 5
 
0.9%
0.472 5
 
0.9%
0.328 5
 
0.9%
0.211 5
 
0.9%
0.399 5
 
0.9%
0.714 4
 
0.7%
0.228 4
 
0.7%
0.112 4
 
0.7%
0.454 4
 
0.7%
Other values (316) 491
91.1%
ValueCountFrequency (%)
0.0374 1
0.2%
0.0382 2
0.4%
0.0499 1
0.2%
0.0567 1
0.2%
0.0586 1
0.2%
0.0633 1
0.2%
0.0662 1
0.2%
0.068 1
0.2%
0.0682 1
0.2%
0.0734 1
0.2%
ValueCountFrequency (%)
0.943 1
0.2%
0.942 1
0.2%
0.928 1
0.2%
0.921 1
0.2%
0.92 2
0.4%
0.917 1
0.2%
0.865 2
0.4%
0.84 2
0.4%
0.838 1
0.2%
0.826 1
0.2%

audio_features.tempo
Real number (ℝ)

Distinct450
Distinct (%)83.6%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean122.36264
Minimum68.097
Maximum208.918
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-01-09T23:22:48.528209image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum68.097
5-th percentile79.025
Q196.6845
median119.0005
Q3143.939
95-th percentile175.61185
Maximum208.918
Range140.821
Interquartile range (IQR)47.2545

Descriptive statistics

Standard deviation30.485522
Coefficient of variation (CV)0.24914077
Kurtosis-0.3552074
Mean122.36264
Median Absolute Deviation (MAD)22.9865
Skewness0.48341329
Sum65831.1
Variance929.36705
MonotonicityNot monotonic
2024-01-09T23:22:48.763209image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
109.993 3
 
0.6%
119.997 3
 
0.6%
70.008 2
 
0.4%
121.05 2
 
0.4%
106.026 2
 
0.4%
114.985 2
 
0.4%
141.929 2
 
0.4%
118.962 2
 
0.4%
119.375 2
 
0.4%
164.004 2
 
0.4%
Other values (440) 516
95.7%
ValueCountFrequency (%)
68.097 1
0.2%
68.534 2
0.4%
70.008 2
0.4%
71.981 1
0.2%
73.849 1
0.2%
73.942 1
0.2%
74.9 1
0.2%
74.952 2
0.4%
74.957 2
0.4%
75.602 1
0.2%
ValueCountFrequency (%)
208.918 1
0.2%
207.476 2
0.4%
204.489 1
0.2%
203.959 2
0.4%
203.89 1
0.2%
202.319 1
0.2%
200.391 1
0.2%
200.017 1
0.2%
199.997 1
0.2%
185.972 1
0.2%
Distinct519
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
2024-01-09T23:22:49.051207image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters11858
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique499 ?
Unique (%)92.6%

Sample

1st row4WUepByoeqcedHoYhSNHRt
2nd row0108kcWLnn2HlH2kedi1gn
3rd row3Vpk1hfMAQme8VJ0SNRSkd
4th row1OcSfkeCg9hRC2sFKB4IMJ
5th row2k0ZEeAqzvYMcx9Qt5aClQ
ValueCountFrequency (%)
4y5bvroubdpr5fuwxbibzr 2
 
0.4%
3rauevgrgj1iuwdj9fds70 2
 
0.4%
1symezit3h8uzfibcs3tyi 2
 
0.4%
6rrnnciqgzexnqk8sq9yv5 2
 
0.4%
1zy1pqizil78gegm4xwlea 2
 
0.4%
1fzauuvbzlhz1ljax9pty6 2
 
0.4%
1smiq65isabpto6gpflbym 2
 
0.4%
43ra71bccxfgd4c8gopiln 2
 
0.4%
1bxfupkguatgp7am0bbdwr 2
 
0.4%
1dgr1c8crmldpv6mpbimsi 2
 
0.4%
Other values (509) 519
96.3%
2024-01-09T23:22:49.717249image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 271
 
2.3%
2 260
 
2.2%
0 252
 
2.1%
1 247
 
2.1%
5 242
 
2.0%
7 242
 
2.0%
3 230
 
1.9%
6 222
 
1.9%
9 209
 
1.8%
y 206
 
1.7%
Other values (52) 9477
79.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4822
40.7%
Uppercase Letter 4695
39.6%
Decimal Number 2341
19.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
y 206
 
4.3%
q 206
 
4.3%
c 205
 
4.3%
z 199
 
4.1%
k 198
 
4.1%
m 198
 
4.1%
x 197
 
4.1%
g 191
 
4.0%
f 191
 
4.0%
w 188
 
3.9%
Other values (16) 2843
59.0%
Uppercase Letter
ValueCountFrequency (%)
Y 196
 
4.2%
V 194
 
4.1%
I 194
 
4.1%
R 193
 
4.1%
U 192
 
4.1%
P 190
 
4.0%
M 189
 
4.0%
A 189
 
4.0%
Q 189
 
4.0%
F 189
 
4.0%
Other values (16) 2780
59.2%
Decimal Number
ValueCountFrequency (%)
4 271
11.6%
2 260
11.1%
0 252
10.8%
1 247
10.6%
5 242
10.3%
7 242
10.3%
3 230
9.8%
6 222
9.5%
9 209
8.9%
8 166
7.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 9517
80.3%
Common 2341
 
19.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
y 206
 
2.2%
q 206
 
2.2%
c 205
 
2.2%
z 199
 
2.1%
k 198
 
2.1%
m 198
 
2.1%
x 197
 
2.1%
Y 196
 
2.1%
V 194
 
2.0%
I 194
 
2.0%
Other values (42) 7524
79.1%
Common
ValueCountFrequency (%)
4 271
11.6%
2 260
11.1%
0 252
10.8%
1 247
10.6%
5 242
10.3%
7 242
10.3%
3 230
9.8%
6 222
9.5%
9 209
8.9%
8 166
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 271
 
2.3%
2 260
 
2.2%
0 252
 
2.1%
1 247
 
2.1%
5 242
 
2.0%
7 242
 
2.0%
3 230
 
1.9%
6 222
 
1.9%
9 209
 
1.8%
y 206
 
1.7%
Other values (52) 9477
79.9%

audio_features.time_signature
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing1
Missing (%)0.2%
Memory size4.3 KiB
4.0
517 
3.0
 
14
5.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1614
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row4.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.0 517
95.9%
3.0 14
 
2.6%
5.0 7
 
1.3%
(Missing) 1
 
0.2%

Length

2024-01-09T23:22:49.950206image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-09T23:22:50.113211image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
4.0 517
96.1%
3.0 14
 
2.6%
5.0 7
 
1.3%

Most occurring characters

ValueCountFrequency (%)
. 538
33.3%
0 538
33.3%
4 517
32.0%
3 14
 
0.9%
5 7
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1076
66.7%
Other Punctuation 538
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 538
50.0%
4 517
48.0%
3 14
 
1.3%
5 7
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 538
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1614
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 538
33.3%
0 538
33.3%
4 517
32.0%
3 14
 
0.9%
5 7
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1614
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 538
33.3%
0 538
33.3%
4 517
32.0%
3 14
 
0.9%
5 7
 
0.4%

artist_id
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
06HL4z0CvFAxyc27GX
539 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters9702
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row06HL4z0CvFAxyc27GX
2nd row06HL4z0CvFAxyc27GX
3rd row06HL4z0CvFAxyc27GX
4th row06HL4z0CvFAxyc27GX
5th row06HL4z0CvFAxyc27GX

Common Values

ValueCountFrequency (%)
06HL4z0CvFAxyc27GX 539
100.0%

Length

2024-01-09T23:22:50.284267image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-09T23:22:50.439248image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
06hl4z0cvfaxyc27gx 539
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1078
 
11.1%
A 539
 
5.6%
G 539
 
5.6%
7 539
 
5.6%
2 539
 
5.6%
c 539
 
5.6%
y 539
 
5.6%
x 539
 
5.6%
F 539
 
5.6%
6 539
 
5.6%
Other values (7) 3773
38.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3773
38.9%
Decimal Number 3234
33.3%
Lowercase Letter 2695
27.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 539
14.3%
G 539
14.3%
F 539
14.3%
C 539
14.3%
L 539
14.3%
H 539
14.3%
X 539
14.3%
Decimal Number
ValueCountFrequency (%)
0 1078
33.3%
7 539
16.7%
2 539
16.7%
6 539
16.7%
4 539
16.7%
Lowercase Letter
ValueCountFrequency (%)
c 539
20.0%
y 539
20.0%
x 539
20.0%
v 539
20.0%
z 539
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6468
66.7%
Common 3234
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 539
8.3%
G 539
8.3%
c 539
8.3%
y 539
8.3%
x 539
8.3%
F 539
8.3%
v 539
8.3%
C 539
8.3%
z 539
8.3%
L 539
8.3%
Other values (2) 1078
16.7%
Common
ValueCountFrequency (%)
0 1078
33.3%
7 539
16.7%
2 539
16.7%
6 539
16.7%
4 539
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9702
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1078
 
11.1%
A 539
 
5.6%
G 539
 
5.6%
7 539
 
5.6%
2 539
 
5.6%
c 539
 
5.6%
y 539
 
5.6%
x 539
 
5.6%
F 539
 
5.6%
6 539
 
5.6%
Other values (7) 3773
38.9%

artist_name
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
Taylor Swift
539 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters6468
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTaylor Swift
2nd rowTaylor Swift
3rd rowTaylor Swift
4th rowTaylor Swift
5th rowTaylor Swift

Common Values

ValueCountFrequency (%)
Taylor Swift 539
100.0%

Length

2024-01-09T23:22:50.705209image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-09T23:22:50.960207image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
taylor 539
50.0%
swift 539
50.0%

Most occurring characters

ValueCountFrequency (%)
T 539
8.3%
a 539
8.3%
y 539
8.3%
l 539
8.3%
o 539
8.3%
r 539
8.3%
539
8.3%
S 539
8.3%
w 539
8.3%
i 539
8.3%
Other values (2) 1078
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4851
75.0%
Uppercase Letter 1078
 
16.7%
Space Separator 539
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 539
11.1%
y 539
11.1%
l 539
11.1%
o 539
11.1%
r 539
11.1%
w 539
11.1%
i 539
11.1%
f 539
11.1%
t 539
11.1%
Uppercase Letter
ValueCountFrequency (%)
T 539
50.0%
S 539
50.0%
Space Separator
ValueCountFrequency (%)
539
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5929
91.7%
Common 539
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 539
9.1%
a 539
9.1%
y 539
9.1%
l 539
9.1%
o 539
9.1%
r 539
9.1%
S 539
9.1%
w 539
9.1%
i 539
9.1%
f 539
9.1%
Common
ValueCountFrequency (%)
539
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6468
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 539
8.3%
a 539
8.3%
y 539
8.3%
l 539
8.3%
o 539
8.3%
r 539
8.3%
539
8.3%
S 539
8.3%
w 539
8.3%
i 539
8.3%
Other values (2) 1078
16.7%

artist_popularity
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
120
539 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1617
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row120
2nd row120
3rd row120
4th row120
5th row120

Common Values

ValueCountFrequency (%)
120 539
100.0%

Length

2024-01-09T23:22:51.172211image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-09T23:22:51.321213image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
120 539
100.0%

Most occurring characters

ValueCountFrequency (%)
1 539
33.3%
2 539
33.3%
0 539
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1617
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 539
33.3%
2 539
33.3%
0 539
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1617
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 539
33.3%
2 539
33.3%
0 539
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1617
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 539
33.3%
2 539
33.3%
0 539
33.3%

album_id
Categorical

Distinct26
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
1MPAXuTVL2Ej5x0JHiSPq8
46 
1NAmidJlEaVgA3MpcPFYGq
 
36
0PZ7lAru5FDFHuirTkWe9Z
 
34
6kZ42qRrzov54LcAk4onW9
 
30
4hDok0OAJd57SGIT8xuWJH
 
26
Other values (21)
367 

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters11858
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1o59UpKw81iHR0HPiSkJR0
2nd row1o59UpKw81iHR0HPiSkJR0
3rd row1o59UpKw81iHR0HPiSkJR0
4th row1o59UpKw81iHR0HPiSkJR0
5th row1o59UpKw81iHR0HPiSkJR0

Common Values

ValueCountFrequency (%)
1MPAXuTVL2Ej5x0JHiSPq8 46
 
8.5%
1NAmidJlEaVgA3MpcPFYGq 36
 
6.7%
0PZ7lAru5FDFHuirTkWe9Z 34
 
6.3%
6kZ42qRrzov54LcAk4onW9 30
 
5.6%
4hDok0OAJd57SGIT8xuWJH 26
 
4.8%
1fnJ7k0bllNfL1kVdNVW1A 24
 
4.5%
6S6JQWzUrJVcJLK4fi74Fw 22
 
4.1%
1KVKqWeRuXsJDLTW0VuD29 22
 
4.1%
1o59UpKw81iHR0HPiSkJR0 22
 
4.1%
5AEDGbliTTfjOB8TSm1sxt 22
 
4.1%
Other values (16) 255
47.3%

Length

2024-01-09T23:22:51.490203image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1mpaxutvl2ej5x0jhispq8 46
 
8.5%
1namidjleavga3mpcpfygq 36
 
6.7%
0pz7laru5fdfhuirtkwe9z 34
 
6.3%
6kz42qrrzov54lcak4onw9 30
 
5.6%
4hdok0oajd57sgit8xuwjh 26
 
4.8%
1fnj7k0bllnfl1kvdnvw1a 24
 
4.5%
6s6jqwzurjvcjlk4fi74fw 22
 
4.1%
1kvkqweruxsjdltw0vud29 22
 
4.1%
1o59upkw81ihr0hpiskjr0 22
 
4.1%
5aedgblittfjob8tsm1sxt 22
 
4.1%
Other values (16) 255
47.3%

Most occurring characters

ValueCountFrequency (%)
1 403
 
3.4%
J 403
 
3.4%
A 353
 
3.0%
V 316
 
2.7%
4 305
 
2.6%
0 291
 
2.5%
T 279
 
2.4%
W 275
 
2.3%
2 269
 
2.3%
P 265
 
2.2%
Other values (52) 8699
73.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5120
43.2%
Lowercase Letter 4308
36.3%
Decimal Number 2430
20.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
J 403
 
7.9%
A 353
 
6.9%
V 316
 
6.2%
T 279
 
5.4%
W 275
 
5.4%
P 265
 
5.2%
S 259
 
5.1%
Z 244
 
4.8%
F 240
 
4.7%
H 233
 
4.6%
Other values (16) 2253
44.0%
Lowercase Letter
ValueCountFrequency (%)
u 263
 
6.1%
c 252
 
5.8%
i 252
 
5.8%
k 238
 
5.5%
q 222
 
5.2%
f 214
 
5.0%
g 209
 
4.9%
o 191
 
4.4%
x 181
 
4.2%
t 177
 
4.1%
Other values (16) 2109
49.0%
Decimal Number
ValueCountFrequency (%)
1 403
16.6%
4 305
12.6%
0 291
12.0%
2 269
11.1%
5 241
9.9%
6 223
9.2%
9 194
8.0%
7 189
7.8%
8 180
7.4%
3 135
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 9428
79.5%
Common 2430
 
20.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
J 403
 
4.3%
A 353
 
3.7%
V 316
 
3.4%
T 279
 
3.0%
W 275
 
2.9%
P 265
 
2.8%
u 263
 
2.8%
S 259
 
2.7%
c 252
 
2.7%
i 252
 
2.7%
Other values (42) 6511
69.1%
Common
ValueCountFrequency (%)
1 403
16.6%
4 305
12.6%
0 291
12.0%
2 269
11.1%
5 241
9.9%
6 223
9.2%
9 194
8.0%
7 189
7.8%
8 180
7.4%
3 135
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 403
 
3.4%
J 403
 
3.4%
A 353
 
3.0%
V 316
 
2.7%
4 305
 
2.6%
0 291
 
2.5%
T 279
 
2.4%
W 275
 
2.3%
2 269
 
2.3%
P 265
 
2.2%
Other values (52) 8699
73.4%

album_name
Categorical

MISSING 

Distinct24
Distinct (%)5.0%
Missing62
Missing (%)11.5%
Memory size4.3 KiB
Lover
36 
folklore: the long pond studio sessions (from the Disney+ special) [deluxe edition]
34 
Red (Taylor's Version)
 
30
Fearless (Taylor's Version)
 
26
Midnights (The Til Dawn Edition)
 
24
Other values (19)
327 

Length

Max length83
Median length26
Mean length24.545073
Min length4

Characters and Unicode

Total characters11708
Distinct characters49
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1989 (Taylor's Version) [Deluxe]
2nd row1989 (Taylor's Version) [Deluxe]
3rd row1989 (Taylor's Version) [Deluxe]
4th row1989 (Taylor's Version) [Deluxe]
5th row1989 (Taylor's Version) [Deluxe]

Common Values

ValueCountFrequency (%)
Lover 36
 
6.7%
folklore: the long pond studio sessions (from the Disney+ special) [deluxe edition] 34
 
6.3%
Red (Taylor's Version) 30
 
5.6%
Fearless (Taylor's Version) 26
 
4.8%
Midnights (The Til Dawn Edition) 24
 
4.5%
1989 (Taylor's Version) [Deluxe] 22
 
4.1%
Speak Now (Taylor's Version) 22
 
4.1%
Red (Deluxe Edition) 22
 
4.1%
Speak Now (Deluxe Package) 22
 
4.1%
1989 (Taylor's Version) 21
 
3.9%
Other values (14) 218
40.4%
(Missing) 62
 
11.5%

Length

2024-01-09T23:22:51.707260image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
version 171
 
10.6%
deluxe 153
 
9.5%
taylor's 121
 
7.5%
edition 119
 
7.4%
the 92
 
5.7%
1989 75
 
4.7%
folklore 67
 
4.2%
fearless 61
 
3.8%
now 58
 
3.6%
speak 58
 
3.6%
Other values (25) 634
39.4%

Most occurring characters

ValueCountFrequency (%)
e 1222
 
10.4%
1132
 
9.7%
o 896
 
7.7%
i 765
 
6.5%
s 709
 
6.1%
l 627
 
5.4%
r 625
 
5.3%
n 606
 
5.2%
a 460
 
3.9%
t 384
 
3.3%
Other values (39) 4282
36.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8278
70.7%
Space Separator 1132
 
9.7%
Uppercase Letter 983
 
8.4%
Close Punctuation 387
 
3.3%
Open Punctuation 387
 
3.3%
Decimal Number 352
 
3.0%
Other Punctuation 155
 
1.3%
Math Symbol 34
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1222
14.8%
o 896
10.8%
i 765
9.2%
s 709
8.6%
l 627
 
7.6%
r 625
 
7.6%
n 606
 
7.3%
a 460
 
5.6%
t 384
 
4.6%
d 372
 
4.5%
Other values (12) 1612
19.5%
Uppercase Letter
ValueCountFrequency (%)
T 184
18.7%
D 143
14.5%
V 137
13.9%
E 85
8.6%
S 81
8.2%
F 69
 
7.0%
N 58
 
5.9%
M 57
 
5.8%
R 52
 
5.3%
L 44
 
4.5%
Other values (3) 73
 
7.4%
Decimal Number
ValueCountFrequency (%)
9 150
42.6%
8 83
23.6%
1 75
21.3%
3 20
 
5.7%
0 16
 
4.5%
2 8
 
2.3%
Close Punctuation
ValueCountFrequency (%)
) 331
85.5%
] 56
 
14.5%
Open Punctuation
ValueCountFrequency (%)
( 331
85.5%
[ 56
 
14.5%
Other Punctuation
ValueCountFrequency (%)
' 121
78.1%
: 34
 
21.9%
Space Separator
ValueCountFrequency (%)
1132
100.0%
Math Symbol
ValueCountFrequency (%)
+ 34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9261
79.1%
Common 2447
 
20.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1222
13.2%
o 896
 
9.7%
i 765
 
8.3%
s 709
 
7.7%
l 627
 
6.8%
r 625
 
6.7%
n 606
 
6.5%
a 460
 
5.0%
t 384
 
4.1%
d 372
 
4.0%
Other values (25) 2595
28.0%
Common
ValueCountFrequency (%)
1132
46.3%
) 331
 
13.5%
( 331
 
13.5%
9 150
 
6.1%
' 121
 
4.9%
8 83
 
3.4%
1 75
 
3.1%
[ 56
 
2.3%
] 56
 
2.3%
+ 34
 
1.4%
Other values (4) 78
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11708
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1222
 
10.4%
1132
 
9.7%
o 896
 
7.7%
i 765
 
6.5%
s 709
 
6.1%
l 627
 
5.4%
r 625
 
5.3%
n 606
 
5.2%
a 460
 
3.9%
t 384
 
3.3%
Other values (39) 4282
36.6%
Distinct23
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
Minimum1989-10-24 00:00:00
Maximum2027-05-26 00:00:00
2024-01-09T23:22:51.906205image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:52.136209image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
Distinct17
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
22
88 
34
64 
16
48 
46
46 
19
38 
Other values (12)
255 

Length

Max length8
Median length2
Mean length2.1521336
Min length1

Characters and Unicode

Total characters1160
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row22
2nd row22
3rd row22
4th row22
5th row22

Common Values

ValueCountFrequency (%)
22 88
16.3%
34 64
11.9%
16 48
8.9%
46 46
8.5%
19 38
 
7.1%
18 36
 
6.7%
17 34
 
6.3%
26 26
 
4.8%
13 26
 
4.8%
24 24
 
4.5%
Other values (7) 109
20.2%

Length

2024-01-09T23:22:52.373210image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
22 88
16.3%
34 64
11.9%
16 48
8.9%
46 46
8.5%
19 38
 
7.1%
18 36
 
6.7%
17 34
 
6.3%
13 26
 
4.8%
26 26
 
4.8%
24 24
 
4.5%
Other values (7) 109
20.2%

Most occurring characters

ValueCountFrequency (%)
2 267
23.0%
1 248
21.4%
4 148
12.8%
6 120
10.3%
3 90
 
7.8%
8 44
 
3.8%
9 38
 
3.3%
0 35
 
3.0%
7 34
 
2.9%
e 30
 
2.6%
Other values (7) 106
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1040
89.7%
Lowercase Letter 105
 
9.1%
Uppercase Letter 15
 
1.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 267
25.7%
1 248
23.8%
4 148
14.2%
6 120
11.5%
3 90
 
8.7%
8 44
 
4.2%
9 38
 
3.7%
0 35
 
3.4%
7 34
 
3.3%
5 16
 
1.5%
Lowercase Letter
ValueCountFrequency (%)
e 30
28.6%
h 15
14.3%
i 15
14.3%
r 15
14.3%
t 15
14.3%
n 15
14.3%
Uppercase Letter
ValueCountFrequency (%)
T 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1040
89.7%
Latin 120
 
10.3%

Most frequent character per script

Common
ValueCountFrequency (%)
2 267
25.7%
1 248
23.8%
4 148
14.2%
6 120
11.5%
3 90
 
8.7%
8 44
 
4.2%
9 38
 
3.7%
0 35
 
3.4%
7 34
 
3.3%
5 16
 
1.5%
Latin
ValueCountFrequency (%)
e 30
25.0%
T 15
12.5%
h 15
12.5%
i 15
12.5%
r 15
12.5%
t 15
12.5%
n 15
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1160
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 267
23.0%
1 248
21.4%
4 148
12.8%
6 120
10.3%
3 90
 
7.8%
8 44
 
3.8%
9 38
 
3.3%
0 35
 
3.0%
7 34
 
2.9%
e 30
 
2.6%
Other values (7) 106
 
9.1%

Interactions

2024-01-09T23:22:35.560707image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:11.091924image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:13.179406image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:15.314668image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:17.420115image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:19.836178image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:21.799743image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:24.319035image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:26.633314image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:28.809296image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:30.661049image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:33.312708image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:35.741752image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:11.246899image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:13.349404image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:15.472669image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:17.598499image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:19.984264image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:21.953571image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:24.494519image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:26.839314image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:28.956294image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:30.810064image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:33.487710image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:35.909712image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:11.443897image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:13.536409image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:15.626669image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:17.777492image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:20.128222image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:22.117034image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:24.661356image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:26.983361image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:29.100293image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:30.953711image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:33.670709image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:36.082711image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:11.600897image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:13.724409image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:15.774522image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:17.983496image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:20.276262image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:22.379040image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:24.902313image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:27.137357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:29.259975image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:31.156713image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:33.836718image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:36.257764image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:11.801905image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:13.915404image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:15.940124image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:18.187540image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:20.438261image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:22.589034image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:25.150311image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:27.303316image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:29.423972image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:31.373707image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:34.005713image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:36.490709image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:11.959001image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:14.071467image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:16.090119image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:18.359493image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:20.573260image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:22.788033image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:25.337309image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:27.579314image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:29.563016image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:31.560709image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:34.237712image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:36.686711image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:12.141004image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:14.286409image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:16.249126image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:18.641494image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:20.726217image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:23.006031image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:25.538354image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:27.745312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:29.719973image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:31.758711image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:34.515709image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:36.879754image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:12.343005image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:14.486413image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:16.467129image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:18.914497image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:20.896226image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:23.335032image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:25.749368image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:27.916367image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:29.894019image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:32.068709image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:34.702706image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:37.051566image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:12.505406image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:14.654409image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:16.664116image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:19.118492image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:21.039704image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:23.517032image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:25.908314image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:28.148309image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:30.045575image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:32.419710image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:34.853713image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:37.245570image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:12.664451image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:14.816404image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:16.824115image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:19.318490image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:21.345710image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:23.700032image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:26.120315image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:28.300316image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:30.204398image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:32.747711image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:35.023708image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:37.416571image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:12.816406image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:15.003407image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:16.973116image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:19.488489image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:21.495706image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:23.868037image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:26.295312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:28.445487image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:30.358240image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:32.925709image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:35.220712image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:37.584999image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:12.969407image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:15.155421image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:17.163116image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:19.657492image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:21.639699image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:24.037030image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:26.453311image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:28.593294image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:30.504276image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:33.112712image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-09T23:22:35.370713image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-01-09T23:22:37.908996image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-09T23:22:38.586786image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

disc_numberduration_msexplicittrack_numbertrack_popularitytrack_idtrack_nameaudio_features.danceabilityaudio_features.energyaudio_features.keyaudio_features.loudnessaudio_features.modeaudio_features.speechinessaudio_features.acousticnessaudio_features.instrumentalnessaudio_features.livenessaudio_features.valenceaudio_features.tempoaudio_features.idaudio_features.time_signatureartist_idartist_nameartist_popularityalbum_idalbum_namealbum_release_datealbum_total_tracks
01212600False1774WUepByoeqcedHoYhSNHRtWelcome To New York (Taylor's Version)0.7570.6107.0-4.84010.03270.0094203.66e-050.36700.685116.9984WUepByoeqcedHoYhSNHRt4.006HL4z0CvFAxyc27GXTaylor Swift1201o59UpKw81iHR0HPiSkJR01989 (Taylor's Version) [Deluxe]2023-10-2722
11231833False2780108kcWLnn2HlH2kedi1gnBlank Space (Taylor's Version)0.7330.7330.0-5.37610.06705.00000000.16800.70196.0570108kcWLnn2HlH2kedi1gn4.006HL4z0CvFAxyc27GXTaylor Swift1201o59UpKw81iHR0HPiSkJR01989 (Taylor's Version) [Deluxe]2023-10-2722
21231000False3793Vpk1hfMAQme8VJ0SNRSkdStyle (Taylor's Version)0.5110.82211.0-4.78500.03970.0004210.01970.08990.30594.8683Vpk1hfMAQme8VJ0SNRSkd4.006HL4z0CvFAxyc27GXTaylor Swift1201o59UpKw81iHR0HPiSkJR01989 (Taylor's Version) [Deluxe]2023-10-2722
31235800False4781OcSfkeCg9hRC2sFKB4IMJOut Of The Woods (Taylor's Version)0.5450.8850.0-5.96810.0447-0.0005375.59e-050.38500.20692.0211OcSfkeCg9hRC2sFKB4IMJ4.006HL4z0CvFAxyc27GXTaylor Swift1201o59UpKw81iHR0HPiSkJR01989 (Taylor's Version) [Deluxe]2023-10-2722
41193289False5772k0ZEeAqzvYMcx9Qt5aClQAll You Had To Do Was Stay (Taylor's Version)0.5880.7210.0-5.57910.03170.00065600.13100.52096.9972k0ZEeAqzvYMcx9Qt5aClQ4.006HL4z0CvFAxyc27GXTaylor Swift1201o59UpKw81iHR0HPiSkJR01989 (Taylor's Version) [Deluxe]2023-10-2722
51219209False67650yNTF0Od55qnHLxYsA5PwShake It Off (Taylor's Version)0.6360.8087.0-5.69310.07290.0121002.18e-050.35900.917160.05850yNTF0Od55qnHLxYsA5Pw4.006HL4z0CvFAxyc27GXTaylor Swift1201o59UpKw81iHR0HPiSkJR01989 (Taylor's Version) [Deluxe]2023-10-2722
61207650False7763FxJDucHWdw6caWTKO5b23I Wish You Would (Taylor's Version)0.6700.8580.0-6.52810.0439-0.0035401.25e-050.06870.539118.0093FxJDucHWdw6caWTKO5b234.006HL4z0CvFAxyc27GXTaylor Swift1201o59UpKw81iHR0HPiSkJR01989 (Taylor's Version) [Deluxe]2023-10-2722
71211103False8757oZONwFiFIErZcXAtTu7FYBad Blood (Taylor's Version)0.6180.6837.0-6.43810.19400.03620000.30500.363169.9717oZONwFiFIErZcXAtTu7FY4.006HL4z0CvFAxyc27GXTaylor Swift1201o59UpKw81iHR0HPiSkJR01989 (Taylor's Version) [Deluxe]2023-10-2722
81220433False97627exgla7YBw9DUNNcTIpjyWildest Dreams (Taylor's Version)0.5890.6748.0-7.48010.06560.0436007.18e-050.11200.514139.98527exgla7YBw9DUNNcTIpjy4.006HL4z0CvFAxyc27GXTaylor Swift1201o59UpKw81iHR0HPiSkJR01989 (Taylor's Version) [Deluxe]2023-10-2722
91247533False1076733OhaXQIHY7BKtY3vnSknHow You Get The Girl (Taylor's Version)0.7580.6915.0-5.79810.05150.0019601.09e-050.09390.538119.997733OhaXQIHY7BKtY3vnSkn4.006HL4z0CvFAxyc27GXTaylor Swift1201o59UpKw81iHR0HPiSkJR01989 (Taylor's Version) [Deluxe]2023-10-2722
disc_numberduration_msexplicittrack_numbertrack_popularitytrack_idtrack_nameaudio_features.danceabilityaudio_features.energyaudio_features.keyaudio_features.loudnessaudio_features.modeaudio_features.speechinessaudio_features.acousticnessaudio_features.instrumentalnessaudio_features.livenessaudio_features.valenceaudio_features.tempoaudio_features.idaudio_features.time_signatureartist_idartist_nameartist_popularityalbum_idalbum_namealbum_release_datealbum_total_tracks
5291207106False6595Tj2MqcFMf60CaGsKbM1aqThe Outside0.5890.8055.0-4.05510.02930.0049100.24000.591112.9825Tj2MqcFMf60CaGsKbM1aq4.006HL4z0CvFAxyc27GXTaylor Swift1205eyZZoQEFQWRHkV2xgAeBwTaylor Swift1989-10-24Thirteen
5301248106False7582zzxwmoOBnXDT0KnJsoIWkTied Together with a Smile0.4790.5782.0-4.96310.02940.5250000.08410.192146.1652zzxwmoOBnXDT0KnJsoIWk4.006HL4z0CvFAxyc27GXTaylor Swift1205eyZZoQEFQWRHkV2xgAeBwTaylor Swift1989-10-24Thirteen
5311236053False85841sjzdjScVwnxnxADElts6Stay Beautiful0.5940.6298.0-4.91910.02460.0868000.13700.504131.59741sjzdjScVwnxnxADElts64.006HL4z0CvFAxyc27GXTaylor Swift1205eyZZoQEFQWRHkV2xgAeBwTaylor Swift1989-10-24Thirteen
5321242200False9656CdaXOq1MWe2JHDalTG01dShould've Said No0.4760.7774.0-3.77100.02890.0103000.19600.472167.9646CdaXOq1MWe2JHDalTG01d4.006HL4z0CvFAxyc27GXTaylor Swift1205eyZZoQEFQWRHkV2xgAeBwTaylor Swift1989-10-24Thirteen
5331213080False10602O8sogKJCfVZ4rotBv1vVFMary's Song (Oh My My My)0.4030.6272.0-5.28010.02920.0177000.18200.37474.9002O8sogKJCfVZ4rotBv1vVF4.006HL4z0CvFAxyc27GXTaylor Swift1205eyZZoQEFQWRHkV2xgAeBwTaylor Swift1989-10-24Thirteen
5341201106False11701j6gmK6u4WNI33lMZ8dC1sOur Song0.6680.6722.0-4.93110.03030.1110000.32900.53989.0111j6gmK6u4WNI33lMZ8dC1s4.006HL4z0CvFAxyc27GXTaylor Swift1205eyZZoQEFQWRHkV2xgAeBwTaylor Swift1989-10-24Thirteen
5351213053False12607CzxXgQXurKZCyHz9ufbo1I'm Only Me When I'm With You0.5630.9348.0-3.62910.06462.000000.0008070.10300.518143.9647CzxXgQXurKZCyHz9ufbo14.006HL4z0CvFAxyc27GXTaylor Swift1205eyZZoQEFQWRHkV2xgAeBwTaylor Swift1989-10-24Thirteen
5361203226False13581k3PzDNjg38cWqOvL4M9vqInvisible0.6120.3947.0-5.72310.02430.6370000.14700.23396.0011k3PzDNjg38cWqOvL4M9vq4.006HL4z0CvFAxyc27GXTaylor Swift1205eyZZoQEFQWRHkV2xgAeBwTaylor Swift1989-10-24Thirteen
5371220146False14580YgHuReCSPwTXYny7isLjaA Perfectly Good Heart0.4830.7514.0-5.72610.03650.0034900.12800.268156.0920YgHuReCSPwTXYny7isLja4.006HL4z0CvFAxyc27GXTaylor Swift1205eyZZoQEFQWRHkV2xgAeBwTaylor Swift1989-10-24Thirteen
5381179066False15581hxLyjC9D9Jpw6EAPKqWv4Teardrops on My Guitar - Pop Version0.4590.75310.0-3.82710.05370.0402000.08630.483199.9971hxLyjC9D9Jpw6EAPKqWv44.006HL4z0CvFAxyc27GXTaylor Swift1205eyZZoQEFQWRHkV2xgAeBwTaylor Swift1989-10-24Thirteen

Duplicate rows

Most frequently occurring

disc_numberduration_msexplicittrack_numbertrack_popularitytrack_idtrack_nameaudio_features.danceabilityaudio_features.energyaudio_features.keyaudio_features.loudnessaudio_features.modeaudio_features.speechinessaudio_features.acousticnessaudio_features.instrumentalnessaudio_features.livenessaudio_features.valenceaudio_features.tempoaudio_features.idaudio_features.time_signatureartist_idartist_nameartist_popularityalbum_idalbum_namealbum_release_datealbum_total_tracks# duplicates
01150440False17721SmiQ65iSAbPto6gPFlBYmIt’s Nice To Have A Friend0.7370.17510.0-9.91210.04010.971000.0003370.17100.54570.0081SmiQ65iSAbPto6gPFlBYm4.006HL4z0CvFAxyc27GXTaylor Swift1201NAmidJlEaVgA3MpcPFYGqLover2019-08-23182
11170640False17743rA71bccXFGD4C8GOpIlNI Forgot That You Existed0.6640.3165.0-10.34510.51900.298002.03e-060.08120.54192.87543rA71bccXFGD4C8GOpIlN4.006HL4z0CvFAxyc27GXTaylor Swift1201NAmidJlEaVgA3MpcPFYGqLover2019-08-23182
21171360False14846RRNNciQGZEXnqk8SQ9yv5You Need To Calm Down0.7710.6712.0-5.61710.05530.0092900.06370.71485.0266RRNNciQGZEXnqk8SQ9yv54.006HL4z0CvFAxyc27GXTaylor Swift1201NAmidJlEaVgA3MpcPFYGqLover2019-08-23182
31173386False6782YWtcWi3a83pdEg3Gif4PdI Think He Knows0.8970.3660.0-8.02910.05690.008890.0003530.07150.416100.0032YWtcWi3a83pdEg3Gif4Pd4.006HL4z0CvFAxyc27GXTaylor Swift1201NAmidJlEaVgA3MpcPFYGqLover2019-08-23182
41190240False11801LLXZFeAHK9R4xUramtUKwLondon Boy0.6950.7101.0-6.63910.05000.024600.0001040.13300.557157.9251LLXZFeAHK9R4xUramtUKw4.006HL4z0CvFAxyc27GXTaylor Swift1201NAmidJlEaVgA3MpcPFYGqLover2019-08-23182
51190360False4863RauEVgRgj1IuWdJ9fDs70The Man0.7770.6580.0-5.19110.05400.0767000.09010.633110.0483RauEVgRgj1IuWdJ9fDs704.006HL4z0CvFAxyc27GXTaylor Swift1201NAmidJlEaVgA3MpcPFYGqLover2019-08-23182
61193000False16802Rk4JlNc2TPmZe2af99d45ME! (feat. Brendon Urie of Panic! At The Disco)0.6100.8300.0-4.10510.05710.0330000.11800.728182.1622Rk4JlNc2TPmZe2af99d454.006HL4z0CvFAxyc27GXTaylor Swift1201NAmidJlEaVgA3MpcPFYGqLover2019-08-23182
71198533False10792dgFqt3w9xIQRjhPtwNk3DDeath By A Thousand Cuts0.7120.7324.0-6.75410.06290.4540000.31900.31394.0712dgFqt3w9xIQRjhPtwNk3D4.006HL4z0CvFAxyc27GXTaylor Swift1201NAmidJlEaVgA3MpcPFYGqLover2019-08-23182
81200306False13785hQSXkFgbxjZo9uCwd11soFalse God0.7390.32011.0-10.86200.23900.736000.0001470.11100.35179.9705hQSXkFgbxjZo9uCwd11so4.006HL4z0CvFAxyc27GXTaylor Swift1201NAmidJlEaVgA3MpcPFYGqLover2019-08-23182
91201586False12724AYtqFyFbX0Xkc2wtcygTrSoon You’ll Get Better (feat. The Chicks)0.4330.1820.0-12.56610.06410.9070000.12300.421207.4764AYtqFyFbX0Xkc2wtcygTr4.006HL4z0CvFAxyc27GXTaylor Swift1201NAmidJlEaVgA3MpcPFYGqLover2019-08-23182